This cross-sectional study analyzes beneficiary cost-sharing payments among Medicare Advantage prescription drug plan and stand-alone Part D plan beneficiaries who reached the out-of-pocket cap in 2025.
The Industrial Internet of Things (IIoT) 's convergence with hybrid cloud infrastructures transforms industrial operations through scalable, real-time data processing and automation. However, this evolution introduces complex cybersecurity risks, including broadened attack surfaces and dynamic trust boundaries. This chapter proposes a cybersecurity framework that integrates AI-driven anomaly detection with Zero Trust Architecture (ZTA) to address these challenges. The framework spans edge, fog, and cloud layers and leverages models like Autoencoders, LSTMs, and Isolation Forests for real-time, contextual threat detection. ZTA principles enforce microsegmentation, identity validation, and adaptive access control. Empirical use case based on the SWaT and WADI datasets validates the framework's efficacy in detecting cyber-physical anomalies. Additionally, the solution aligns with standards such as NIST 800-207, IEC 62443, and ISO/IEC 27001, offering practical guidance for secure, adaptive IIoT deployments.
Introduction This study sought to define the leadership skills and competencies that are essential to physician leaders and current gaps in physician leadership training. The study also aimed to identify the skills that current physician leaders would have liked to develop earlier in their careers and generate recommendations for successfully integrating leader development into medical education. Methods This qualitative study explored healthcare leader development through semi-structured interviews with 14 healthcare executives from major healthcare systems in the Southeastern United States. Participants were selected based on their high-level leadership roles. Interviews were conducted virtually and focused on three areas: critical leadership skills, reflections on early career development needs, and strategies for integrating leadership training into medical education. Transcripts were thematically analyzed by three independent investigators to identify recurring themes and insights. Results Healthcare leaders identified six essential leadership skills: adaptability, empathy and gratitude, relational leadership, change leadership, project management, and knowledge of the healthcare system. Common gaps that emerging leaders should address include relational leadership, redefining leadership beyond hierarchy, self-awareness, understanding the healthcare system, and financial acumen. To prepare future physician leaders, interviewees emphasized the need for training in professional identity formation as a leader, experiential learning, interdisciplinary collaboration, self-awareness, and resilience. Several themes, including relational leadership, healthcare system knowledge, self-awareness, and business acumen, emerged across multiple domains. Conclusions Healthcare leadership requires more than clinical expertise alone. The role demands a blend of interpersonal, adaptive, and technical skills to navigate complex challenges. Given the growing demands placed upon healthcare leaders, medical institutions must integrate structured leadership training early in physicians' careers. Preparing future leaders through intentional education and mentorship will help ensure that the healthcare system remains effective and responsive amid significant external pressures and internal transformations.
Objective Clinical prediction models trained on electronic health records are routinely evaluated for fairness on observed feature values, but the informativeness of which measurements are absent remains unaudited. We developed the Missingness Demographic Leakage Audit (MDLA), a reproducible four-step informatics framework that tests whether patterns of clinical measurement absence function as latent demographic proxies — constituting a bias pathway invisible to standard fairness audits. Materials and Methods We applied MDLA across development (MIMIC-IV v2.2; n=50,827; mortality 10.2%) and external validation (eICU-CRD v2.0; n=137,773; mortality 9.5%) cohorts following TRIPOD+AI standards. XGBoost, random forest, and logistic regression were trained on 43 clinical features and 44 binary missingness indicators. MDLA quantified demographic predictability from missingness alone, tested feature-level associations with Bonferroni correction, and verified model reliance via ablation. A calibration-aware fairness audit evaluated five criteria across four demographic axes; six post-hoc recalibration strategies were compared on a fairness-utility Pareto frontier. Results Missingness indicators alone predicted racial group membership above chance (AUROC=0.543; 95% CI, 0.540–0.546), with 18 of 43 features showing Bonferroni-significant race-missingness associations (all Cramér's V<0.10). Ablation confirmed model reliance: adding missingness indicators increased racial AUROC disparity by 10.7% (0.063 to 0.069) without improving global performance. XGBoost achieved AUROC=0.910 internally (AUROC=0.799 on external validation). Global Platt recalibration reduced overall calibration error by 94% and maximum racial calibration error by 51%, with zero AUROC loss and successful parameter transfer to external validation without retraining. Conclusion MDLA provides a structured, reproducible protocol for detecting missingness-encoded demographic signals prior to model deployment. Applied across 188,600 ICU patient-stays from two institutionally diverse databases, it identified a statistically confirmed but subtle bias pathway undetectable by standard fairness audits. Missingness-aware auditing and calibration-aware evaluation should be integrated into clinical AI validation pipelines.
Management research is frequently criticized for lacking practical relevance. Using voluntary turnover as a mature and managerially consequential research domain, we review 324 articles published between 2000 and 2023 and systematically extract 493 distinct practical recommendations. In advancing prior assessments of the relevance problem, we consider practical relevance at the recommendation-level of analysis, evaluating each recommendation along two core dimensions: data support (grounding in the study’s own empirical findings) and translatability (actionability for managers). We further situate recommendations within the turnover management process by coding for timing, level of action, and managerial goal of the recommendation. Our findings suggest that the relevance problem is less serious in the turnover research than commonly portrayed in the “relevance literature” regarding the management research in general. Although most studies included turnover management recommendations, only a small subset was both strongly grounded in the study’s evidence and readily actionable, with such high-quality guidance most concentrated on pre-hire interventions aimed at reducing overall turnover rates. In contrast, one of the gaps we detected was that such high-quality guidance is rarer for later-stage turnover management interventions focusing on reducing dysfunctional turnover. By contrasting high versus lower quality recommendations, we develop a five-step framework to help management scholars translate empirical findings into clear, context-sensitive, and actionable managerial guidance. Overall, we contribute to management scholarship by offering a more precise recommendation-level assessment of the relevance problem for use across areas, by identifying content-based gaps inhibiting improvement of practical relevance within the turnover research, and by improving the communication of practical recommendations in research articles.
